EP2750078B1 - Three-dimensional data processing and recognizing method - Google Patents

Three-dimensional data processing and recognizing method Download PDF

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Publication number
EP2750078B1
EP2750078B1 EP13199463.4A EP13199463A EP2750078B1 EP 2750078 B1 EP2750078 B1 EP 2750078B1 EP 13199463 A EP13199463 A EP 13199463A EP 2750078 B1 EP2750078 B1 EP 2750078B1
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EP
European Patent Office
Prior art keywords
interest
point
target
plane
data
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Not-in-force
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EP13199463.4A
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German (de)
English (en)
French (fr)
Other versions
EP2750078A2 (en
EP2750078A3 (en
Inventor
Mingliang Li
Zhiqiang Chen
Yuanjing Li
Li Zhang
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tsinghua University
Nuctech Co Ltd
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Tsinghua University
Nuctech Co Ltd
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Priority to PL13199463T priority Critical patent/PL2750078T3/pl
Publication of EP2750078A2 publication Critical patent/EP2750078A2/en
Publication of EP2750078A3 publication Critical patent/EP2750078A3/en
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Publication of EP2750078B1 publication Critical patent/EP2750078B1/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/64—Three-dimensional objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/05—Recognition of patterns representing particular kinds of hidden objects, e.g. weapons, explosives, drugs

Definitions

  • the present invention relates to three-dimensional data processing and recognizing field, and more particularly, to intelligent recognition of prohibited articles in security inspection apparatuses by CT or to be detected articles in nuclear magnetic resonance systems.
  • the reference WO 00/42566A1 provides an apparatus and a method for identifying and classifying objects represented in computed tomography (CT) data for a region using the shape of the objects.
  • An object represented by CT data for a region is identified.
  • a two-dimensional projection of the object along a principal axis of the object is generated.
  • the principal axis is identified by computing eigenvectors of a covariance matrix of spatial locations of voxels in the CT data that are associated with the object.
  • the smallest eigenvector can be selected as the principal axis of the object along which the two-dimensional projection is generated.
  • the identification of the object can be used in the classification of the object such as by altering one or more discrimination parameters.
  • an object of the present invention is to alleviate the above problems and defects in the prior arts.
  • the invention provides a three-dimensional data processing and recognizing method having the features as recited in claim 1.
  • Advantageous embodiments are set out in the dependent claims.
  • the invention provides a new three-dimensional data processing and recognizing method, comprising the following steps of:
  • the general concept of the present invention is that the three-dimensional data obtained by scanning and re-constructing of the insecurity inspection CT system are searched, extracted, merged, counted and recognized on terms of shapes, by feature data of the suspect object or the targets of interest, for example, dangerous articles or prohibited articles.
  • image segmentation of the three-dimensional data in the prior art is still relatively difficult up to now, and accuracy and generality thereof are not so good, starting from another viewpoint the present invention provides the present three-dimensional data processing and recognizing method which solves the problems of searching, making statistics and recognizing on terms of shape for the targets of interest.
  • an embodiment of the present invention explains how to recognize prohibited articles or dangerous articles by a three-dimensional data processing and recognizing method of the present invention.
  • the present three-dimensional data processing and recognizing method is also applicable to a nuclear magnetic resonance system or the like, so as to recognize the targets of interest.
  • the present general concept is to perform searching, extraction, mergence, statistics and/or shape recognition on the three-dimensional data obtained by scanning and re-constructing of the security inspection CT system by means of using feature data of the targets of interest or suspected targets, for example, dangerous articles or prohibited articles.
  • the present invention proposes out the present three-dimensional data processing and recognizing method from a different viewpoint thereto. Further, the present invention solves technical problems on searching, statistics and shape recognition with respect to the targets of interest.
  • the present invention mainly applies to recognition of dangerous articles such as a cone or an approximate cone, for example, sharp, pointed, or arrowed dangerous articles or spike-like murder weapons.
  • the present recognizing method is also applicable to cylinder-shaped articles, such as guns or the like.
  • a cone is taken as one example, but it should be understood that a cylinder also has the similar properties. Therefore, the present three-dimensional data processing and recognizing method can be used to recognize the cone and/or the cylinder.
  • a cone is cut through an axis AG thereof to obtain a plane ⁇ (specifically, shown as a triangle ABC).
  • the plane ⁇ is intersected with a cross section of the cone and the intersection line is labelled as EF.
  • the intersection line EF is perpendicular to the axis AG, i.e., EF ⁇ LAG.
  • the plane ⁇ is intersected with an inclined cross section of the cone, at an intersection line labelled as JK.
  • JK intersection line labelled as JK.
  • a line LM is parallel to the line JK, i.e., LM//JK.
  • a point P is a midpoint of the line segment JK
  • the line AP is intersected with the line LM at a point Q.
  • the point Q is a midpoint of the line segment LM. Since ⁇ APK ⁇ ZADK and ⁇ ADK ⁇ ZADF, it can be derived: ⁇ APK ⁇ ZADF, i.e., ⁇ APK ⁇ 90°. Therefore, the present invention adopts such property of the cone as a judgement basis to recognize its cross section. Specifically, the present three-dimensional data processing and recognizing method is illustrated as follows.
  • the present three-dimensional data processing and recognizing method mainly includes two parts: data processing and recognition on terms of shapes and size of the cone/cylinder.
  • the security inspection CT system scans the object to be detected and re-constructs it to obtain CT image data (i.e., three-dimensional data). And then, prohibited articles (such as cutting tools) may be recognized based on the obtained three-dimensional data.
  • the prohibited articles can be common dangerous goods, such as cutting tools, explosives, guns and the like.
  • the feature data can be data about composition materials of the common dangerous articles, for example, iron, copper, or heavy metals.
  • Such data can be any one of attenuation coefficient data, density data and atomic number data (or energy spectrum data of energy spectrum CT), and any combination thereof.
  • the data can be density data and/or the atomic number data.
  • adjacent data points are merged and classified as one group. Images of these merged three-dimensional data can be displayed individually or highlighted, for inspector's inspection.
  • a number of data points among the merged three-dimensional data can be counted to estimate dimension of the prohibited articles.
  • statistics on mass, position, density and atomic number where the merged three-dimensional data are located to respectively derive mean values thereof, so that more specific information on position, volume, mass, material species of the targets of interest located in the articles to be inspected can be obtained.
  • whether the articles to be detected or inspected are drugs or explosives, can be recognized in dependency of their density and atomic number.
  • the targets of interest formed by the extracted three-dimensional data or the targets of interest formed by the merged three-dimensional data can be used to perform the step of constructing surface data from the three-dimensional data, before a step of recognizing in shape and size.
  • the step of "constructing surface data from the three-dimensional data” is described in detail. It should be understood that the step of "constructing surface data from the three-dimensional data” can also be performed on basis of the targets of interest formed by the extracted three-dimensional data.
  • the merged three-dimensional data constitute or make up a three-dimensional target (i.e., a target of interest).
  • a method of selecting the point I includes : selecting a maximal value and a minimal value of the data within the merged three-dimensional data in directions of x, y, z axes of rectangular coordinate system (herein, the direction of x, y, z axes of rectangular coordinate system can be arbitrarily selected, as long as any two of the three directions are perpendicular to each other, similarly hereinafter); and respectively calculating median of the maximal value and the minimal value in the above described three directions, wherein coordinates of the median can be selected as coordinates of the selected point I.
  • a spherical coordinate system is established with the point I as origin of coordinate.
  • an angle ( ⁇ , ⁇ ) of each point among the merged solid data (the three-dimensional data) and a distance thereof from the point I are calculated, wherein 0 ⁇ , 0 ⁇ 2 ⁇ (in a physical sense, ⁇ is a zenith angle, and ⁇ is an azimuthal angle).
  • ⁇ and sin ⁇ are set and a point within each solid angle sin ⁇ , which is farthest away from the point I, is selected as one of surface data.
  • some data out of the surface can be eliminated.
  • One preferable means is to obtain an interpolation point of each surface datum(corresponding to each data point) as described above, which is obtained by interpolating surrounding points of the selected point. If a distance of the interpolation point from the point I is larger than a distance of the selected point from the point I, then the selected point which has been considered as a surface data point will be eliminated. Specifically with reference to figure 2 (showing out four points A, B, C, and D), since the distance of the interpolation point of the point B derived by linear interpolation of points A and C from the point I is larger than the distance of point B from the point I, the point B is eliminated.
  • the left data can be considered as more reliable surface data.
  • the constructed surface data can be showed to the operator, so as to intuitively and clearly observe shape of the target of interest and perform an initial recognition (for example, in shape).
  • Another method to construct the surface data is: as for an target of interest, each of three-dimensional data is analyzed; if there are additional data adjacent to it in all three dimensional directions (for example, the upward and downward direction, the left and right direction, and the fore-and-aft direction), then the three-dimensional data can be deemed to be non-surface data; in contrast, the remaining three-dimensional data shall be surface data.
  • the target of interest to be recognized as the cone/cylinder not only can be the target of interest formed by the three-dimensional data (i.e., the merged three-dimensional data) obtained in the step of "data processing”, but also can be the target of interest formed by the three-dimensional data obtained in the step of "constructing surface data".
  • the recognition and processing are performed on the target of interest formed after the step of "constructing surface data from three-dimensional data". It should be understood that the target of interest in the latter case contains less redundant data, and thus is easy to be processed, because some data points out of the surface have been eliminated.
  • a point I is arbitrarily selected inside the target of interest. It is preferable to select the coordinate (x 0 , y 0 , z 0 ) of a median of a maximal value and a minimal value of the three-dimensional data in a direction of respective three x, y, and z coordinate axes as the coordinate of the point I.
  • a method of recognizing a cross section of the target of interest includes the following steps of:
  • the object to be detected has a shape like a cylinder, cone or truncated cone
  • the following quick recognizing method can be used to search the cross section (of course, the above method is still applicable, but its calculating speed may be relatively slow).
  • a plane a passing through the point I within the target of interest (supposing its normal direction being a spherical coordinate direction ( ⁇ , ⁇ )) is used to cut the target of interest. If the cut graph is a circle, then the plane a is the cross section. If not, the normal direction ( ⁇ , ⁇ ) of the plane a (passing through the point I) is transformed to find out a new plane considered as the cross section, which is used to cut the target of interest again. This step is repeated until the cut graph is a circle (i.e., obtaining the cross section), or if the normal direction ( ⁇ , ⁇ ) goes through all the possible directions, the step will stop.
  • the cross section a of the target of interest is found out.
  • Two planes ⁇ and ⁇ perpendicular to the cross section a are made, which pass through the central point of the cross section a.
  • the two planes ⁇ and ⁇ are perpendicular to each other. (for the sake of saving calculation time, it is also possible to only draw one such plane perpendicular to the cross section ⁇ )
  • the target of interest is determined to be a cone.
  • a point angle of the cone is a vertex angle of the triangle.
  • the graph obtained through cutting the target of interest by the perpendicular planes ⁇ and ⁇ is a trapezium
  • a line segment TU(consisting of two of four end points X, Y, U and T of the trapezium, as shown in figure 3 ) is larger than or equal to a line segment XY(consisting of the other two of four end points X, Y, U and T of the trapezium), i.e., the line segment TU ⁇ the line segment XY, a ratio ⁇ (TU-XY) / (TU+XY) ⁇ of a difference of lengths of the line segments TU and XY to a summation of lengths of the line segments TU and XY is calculated.
  • the target of interest is determined to be a cone; otherwise if the ratio is less than the threshold, then the target of interest is determined to be a cylinder.
  • a threshold for example, 0.1
  • the end points X and Y are upper end points of the trapezium
  • the end points U and T are lower end points of the trapezium.
  • the graph obtained by cutting the truncated cone or the frustum of pyramid by the perpendicular plane ⁇ or ⁇ typically is a trapezium.
  • the graph obtained through cutting the target of interest by the perpendicular plane ⁇ is a trapezium XYTU or a triangle ATU.
  • a length of the line segment XY is calculated as dimension of a conical tip of the cone.
  • An intersection angle between the line segment XT and the line segment YU is calculated as a point angle of the cone.
  • such point angle can be obtained by a formula of 2sin -1 (TU-XY)/2XT).
  • TU-XY 2sin -1
  • XT 2sin -1
  • the system will be informed to make an alert.
  • a threshold for example, 8mm
  • another threshold for example, 90°
  • the target of interest can be determined as the bullet.
  • the target of interest is determined as a cylinder.
  • the dimension of the cylinder can be calculated from length and width of the rectangular, as shown in figure 5 .
  • the figure 5 shows the graph obtained by cutting the target of interest can be a rectangular, when the target of interest with a shape of the cylinder or the cube is cut by the perpendicular plane ⁇ .
  • two obtained graphs through cutting the target of interest by them are rectangular having an equal size, and if the cross section of the target of interest is of a circle, then the target of interest is recognized as a cylinder.
  • the target of interest is determined to be tube-shaped.
  • the tube-shaped target has an inner diameter in a range of an inner diameter of a gun barrel, and the tube-shaped target is of metal material (such as copper, iron), then the target of interest is determined to be a gun.
  • metal material such as copper, iron
  • the target of interest is determined to be a cuboid.
  • the present method can also recognize the objects such as an arrow, a pointed knife, a screwdriver, a cannonball, and a missile warhead or the like.
  • the cone or arrow of glass material is also a potential dangerous article.
  • a needle-shaped object or the like can be recognized in a high-precision image.
  • the material thereof is of metal and the size thereof meets the corresponding requirements, then it is determined to be a missile body or a cannonball body.
  • a filling material contained therein or disposed adjacent thereto conform to features of ammunition (for example, density or atomic number), a presence of a high dangerous missile or cannonball body can be accurately determined.
  • an intelligent inspection can be performed, so as to effectively prevent very common prohibited carrying articles such as long stick and a crutch in aviation security.
  • the working intensity of the inspector is decreased from a viewpoint of human.
  • the present method herein is applicable to systems for generating three-dimensional data, such as CT and nuclear magnetic resonance systems.
  • a rectangular coordinate system and a spherical coordinate system are used to explain the method, but usage of other coordinate systems or a simple extension of the present invention all falls within the scope of the present invention.
  • the present invention extracts three-dimensional data by matching features, and recognizes a cross section of the target of interest; cuts the targets of interest by at least one perpendicular plane which passes through a central point of the cross section and is perpendicular to it, in order to obtain a graph; and recognizes shapes of the targets of interest based on property of the graph, so that the prohibited articles such as spike-shaped objects or guns can be recognized.
  • CT systems can employ the present method to recognize the dangerous articles.
  • Other systems generating three-dimensional data, such as nuclear magnetic resonance system or the like can introduce this method to recognize the target of interest.
  • the present invention provides the present three-dimensional data processing and recognizing method which solves the problems of searching, making statistics and recognizing on terms of shape for the targets of interest.
  • a method of the present invention is applicable to systems generating three-dimensional data such as CT and nuclear magnetic resonance system, and so on.
  • a rectangular coordinate system and a spherical coordinate system are taken as examples to describe the three-dimensional data processing and recognizing method of the present invention, it should be understood that using other coordinate systems based on the present invention or simple development of the present invention all fall within the scope of the present invention.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Image Analysis (AREA)
  • Analysing Materials By The Use Of Radiation (AREA)
  • Image Generation (AREA)
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EP13199463.4A 2012-12-27 2013-12-23 Three-dimensional data processing and recognizing method Not-in-force EP2750078B1 (en)

Priority Applications (1)

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PL13199463T PL2750078T3 (pl) 2012-12-27 2013-12-23 Sposób przetwarzania i rozpoznawania danych trójwymiarowych

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CN201210581559.9A CN103903298B (zh) 2012-12-27 2012-12-27 三维数据处理和识别方法

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EP2750078A2 EP2750078A2 (en) 2014-07-02
EP2750078A3 EP2750078A3 (en) 2016-06-01
EP2750078B1 true EP2750078B1 (en) 2018-09-12

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US (1) US9111128B2 (zh)
EP (1) EP2750078B1 (zh)
JP (1) JP6013621B2 (zh)
KR (1) KR101813771B1 (zh)
CN (1) CN103903298B (zh)
PL (1) PL2750078T3 (zh)
WO (1) WO2014101387A1 (zh)

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CN103903297B (zh) 2012-12-27 2016-12-28 同方威视技术股份有限公司 三维数据处理和识别方法
WO2016052489A1 (ja) * 2014-09-29 2016-04-07 株式会社Ihi 画像解析装置、画像解析方法及びプログラム
CN106296797A (zh) * 2015-06-10 2017-01-04 西安蒜泥电子科技有限责任公司 一种三维扫描仪特征点建模数据处理方法
US10621406B2 (en) * 2017-09-15 2020-04-14 Key Technology, Inc. Method of sorting
CN113128346B (zh) * 2021-03-23 2024-02-02 广州大学 起重机施工现场的目标识别方法、系统、装置及存储介质
CN114608450B (zh) * 2022-03-10 2023-09-26 西安应用光学研究所 机载光电系统对远距离海面目标三维尺寸测算方法

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Publication number Publication date
PL2750078T3 (pl) 2019-02-28
JP2016505976A (ja) 2016-02-25
US9111128B2 (en) 2015-08-18
WO2014101387A1 (zh) 2014-07-03
EP2750078A2 (en) 2014-07-02
KR20150103160A (ko) 2015-09-09
CN103903298B (zh) 2017-03-01
KR101813771B1 (ko) 2017-12-29
CN103903298A (zh) 2014-07-02
JP6013621B2 (ja) 2016-10-25
US20140185874A1 (en) 2014-07-03
EP2750078A3 (en) 2016-06-01

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